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Brian J. Hoskins and Kevin I. Hodges

example, cyclonic and anticyclonic systems. These methods range from fairly simple nearest-neighbor tracking methods with simple grid box statistics to more sophisticated approaches of tracking and statistical estimation. In this paper data from the European Centre for Medium-Range Weather Forecasts (ECMWF) have been used for the Northern Hemisphere (NH) winter (December to February). Results will be shown from the application of the filtered variance and tracking techniques to a wide variety of

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Greg J. Holland

North Pacific ( Fig. 4 ). Further, we suggest that empirical relationships between environmental parameters, such as vertical wind shear, establishment of outflow jets, etc. (e.g., Merrill 1993 ), need to be revised to accommodate the strong thermodynamic inhibitions on cyclone intensity described in this study. The method is straightforward to apply, requiring only a sounding and surface pressure and temperature, and is applicable to daily estimates of MPI based on operational numerical model

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Richard H. Johnson and Paul E. Ciesielski

-derived rainfall will be compared to results from the National Centers for Environmental Prediction (NCEP) reanalysis ( Kalnay et al. 1996 ) and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis ( Gibson et al. 1997 ). An evaluation will be given of the reliability of the NCEP and ECMWF reanalysis meridional rainfall distributions over the COARE domain. Last, the vertically integrated heat and moisture budgets will be combined to determine the tropospheric-averaged net radiative heating

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Irving I. Gringorten

is less than 0.37, its operational superiorityis not accepted with finality. But we make this statement only with respect to the operation representedby the scores of table 5.3, TheoryThe test of this article is designed for the kind offorecasting in which the forecaster chooses as hisTABLE 5. Scores for hypothetical operational requirement.ForecastObserved Rain No rainRain 3 -2No rain 0 1APRIL 1955 IRVING I. GRINGORTEN 183forecast one event out of a classification of

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Arthur Y. Hou and Sara Q. Zhang

operational forecasts ( Treadon et al. 2002 ; Aonashi et al. 2004 ; Marecal and Mahfouf 2003 ; Bauer et al. 2006 ). Currently, precipitation information (either retrievals or rain-affected radiances) is assimilated in NWP systems much the same way as any other data to optimize the initial state for a better forecast. To this end, the system requires an “observation operator” capable of relating the observable to the initial state with reasonable accuracy. In this regard, precipitation assimilation

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David M. Schultz

within this comment. First, operational forecasters in the U.S. National Weather Service sometimes discuss the importance of midlatitude cumulus congestus clouds leading to moistening of dry air above the planetary boundary layer and the eventual development of deep moist convection. The argument posed by Hohenegger and Stevens (2013) is for the tropics. The question remains how applicable their ideas are for the midlatitudes. Second, Hohenegger and Stevens (2013) use the term “moisture

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John O. Roads

to examine andimprove operational forecasts, (see DHK) it wouldseem prudent to examine these questions exhaustivelywith sim_ pler models. The advantage to using a simplermodel is that large ensembles and forecast times canbe examined. This exhaustive examination can thenprovide a useful starting point for examining the morelimited datasets of operational forecasts. It also helpsto answer the question as to whether the skill of a laggedaverage forecast i's due to imperfections in the

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Tomislava Vukicevic, Eric Uhlhorn, Paul Reasor, and Bradley Klotz

terms of maximum near-surface wind speed within the storm ( Rappaport et al. 2009 ; Jarvinen et al. 1984 ; Harper et al. 2009 ). The standard operational forecast skill analysis in the United States ( http://www.nhc.noaa.gov/verification ) indicates that over the last two decades, there has been a marked improvement in operational forecasting of TC tracks, while the intensity forecast skill has exhibited virtually no improvement over the same period ( Rappaport et al. 2009 ). The increased

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John A. Knox, Donald W. McCann, and Paul D. Williams

regarding our focus on the source, not the far-field propagation, of gravity waves: operational CAT forecasting algorithms have never attempted to forecast CAT by predicting the motion of gravity wave trains (e.g., ray-tracing techniques). Practical considerations have always limited forecasting approaches to the hypothesis that aircraft encounter turbulence near regions of strong forcing. This may be surprising to dynamicists, but it is still the state of the art in CAT forecasting and therefore was

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David Kuhl, Istvan Szunyogh, Eric J. Kostelich, Gyorgyi Gyarmati, D. J. Patil, Michael Oczkowski, Brian R. Hunt, Eugenia Kalnay, Edward Ott, and James A. Yorke

the forecast error covariance matrix. The lowest possible value of the E dimension, which is one, occurs when the estimated variance is confined to a single spatial pattern of uncertainty. The highest possible value of the E dimension, which is equal to the number of ensemble members N , occurs when the variance is evenly distributed between N independent patterns of uncertainty. Patil et al. (2001) applied the E dimension diagnostic to operational forecast ensembles of the National Centers

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